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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi
Ario Yudo Husodo, Ario Yudo
Departmet of Informatics Engineering, Engineering Faculty, Mataram University
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PERFORMANCE COMPARISON OF NAIVE BAYES AND BIDIRECTIONAL LSTM ALGORITHMS IN BSI MOBILE REVIEW SENTIMENT ANALYSIS Ma'we, Hannatul; Husodo, Ario Yudo; Irmawati, Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4178

Abstract

Currently, almost all banks have used mobile banking in conducting banking transactions, one of which is Bank Syariah Indonesia (BSI). BSI mobile is still classified as a new mobile banking application compared to other mobile banking, this certainly still has a low rating and really needs feedback from users which can be seen through reviews on the Google Play Store application. Input in the form of criticism and suggestions from BSI mobile users can be used by BSI mobile as a suggestion for careful supervision and evaluation material in improving its services. This study aims to find the best algorithm to analyze review sentiment on the Google Play Store for the BSI mobile application and provide an overview of the response of application users to application developers based on the results of review data processing. The data mining methodology used in this study is CRISP-DM, using a dataset collected for 6 years (2018-2023) which is annotated into positive and negative labels manually, then modeled using 2 algorithms, namely Naïve Bayes (NB) and Bidirectional LSTM (BiLSTM). The contribution of this study is to test, evaluate and compare the two algorithms (NB and BiLSTM) using the K-Fold Cross Validation (NB) testing model and over-sampling techniques to the minority class (negative) then provide recommendations for the best algorithm. The conclusion of the study is that the BiLSTM algorithm is superior to NB with an accuracy of 94.90 % while the NB algorithm is 94%. In addition, the over-sampling technique is more optimal in increasing the accuracy of the algorithm's performance compared to without over-sampling.
IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING CONTENT-BASED FILTERING Husodo, Ario Yudo; Bimantoro, Fitri; Agitha, Nadiyasari; Grendis, Nuraqilla Waidha Bintang
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4194

Abstract

NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.
Co-Authors Afwani, Royana Agitha, Nadiyasari Budi Irmawati Dwiyansaputra, Ramaditia Fitri Bimantoro Grendis, Nuraqilla Waidha Bintang Hanan, Dimas Afryzal I B K Widiartha I Wayan Agus Arimbawa, I Wayan Agus Ma'we, Hannatul Muhammad, David Arizaldi Muntari Muntari Murpratiwi, Santi Ika Rahayu, Sefani Cahyo Auliya Rassy, Regania Pasca Sugianti, Novalia Dwirohmatun